1) Background / Theory

Understanding the Fundamentals: Theory and Background

Time: 09:00-09:40

This introduction provides a concise overview of dynamic systems in neuroscience, emphasizing their significance and practical applications. It covers fundamental concepts and tools, examining dynamic behaviors across various degrees of freedom with examples from computational neuroscience. The course explores intricate mechanisms like criticality, bifurcations, and symmetry breaking, highlighting their roles in shaping complex behaviors in neuronal networks. It addresses self-organization phenomena within these networks, shedding light on the organizational principles governing neural systems. Additionally, the course makes the connection to probability density distributions, Free Energy, and the Maximum Information Principle. It elucidates the interplay of deterministic and stochastic forces, offering a nuanced understanding of their impact on neural dynamics. In summary, this lecture delivers a comprehensive introduction to dynamical systems in neuroscience, enhancing our understanding of neural phenomena. It shall serve as a valuable resource for scholars, researchers and students studying the mechanisms of neural networks.

Presenter

Viktor Jirsa
Institut de Neurosciences des Systèmes Marseille
France


Criticality, metastability, multistability: The “primitives” of complex brain dynamics

Time: 09:40-10:20

Considerable research suggests that multi-scale processes in the brain arise from so-called critical phenomena that occur broadly in nature. Criticality occurs in systems perched between order and disorder, allowing agents to quickly adapt to a dynamic environment. But criticality comes in several flavours that possess unique computational properties, namely bifurcations, metastability and multistability. Each of these complex dynamics builds on simple underlying dynamical processes in different ways to broaden the behavioural repertoire of dynamical systems. I will illustrate these distinct types of criticality in simple neural mass models and overview methods to detect and disambiguate them in functional neuroimaging data. I will also summarise their explanatory potential in brain health and disease, ranging from natural vision, motor behaviour, decision making, seizures and hallucinations.

Presenter

Michael Breakspear
University of Newcastle Newcastle
Australia


Introduction to connectome-based neural mass modelling

Time: 10:20-11:00

Mathematical models of human brain activity have been central in gaining insights into the hidden mechanisms of the underlying neural processes at multiple scales. In this context, Whole-Brain Modelling (WBM) is a sub-field of computational neuroscience concerned with building comprehensive theoretical and computational models that represent and simulate the neural activity across the entire brain. The common objective of this approach is to investigate the mechanisms through which macroscopic spatiotemporal patterns of neural activity can be explained by studying the interplay among anatomical connectivity structure, intrinsic neural dynamics, and external perturbations (sensory, cognitive, pharmacological, electromagnetic, etc). Such macroscopic phenomena (i.e brain oscillations), and models thereof, are of particular scientific interest because a) large scale neural activity can be most readily obtained from the brains of healthy human subjects, using noninvasive neuroimaging and related methods b) they represent neural systems in a holistic and relatively intact state, . Simulations of human brain activity, in both health and disease, are therefore a principal focus of current WBM research. The overarching idea is to model the brain at the macroscale as a network of interconnected regions, which are defined by (principally) neuroimaging-based brain parcellations The presence and weights of the network edges interconnecting the nodes are then derived from neuroimaging- or chemical tract tracing-based anatomical connectivity measurements. The nodes can be described using neural mass models (NMM) which represent the coarse grained activity of large populations of neurons and synapses using a small number of equations to express their mean firing rates and mean membrane potentials. NMM are capable of describing the change in firing rate of neural populations without spatial information and spatiotemporal time delays providing a succinct yet biophysically meaningful description of brain activity at the mesoscopic scale to reflect phenomena observed empirically at the macroscale. The main advantage of NMM is that the simplification of the dynamics reduces the number of dimensions or differential equations that need to be integrated enabling us to hone in on the behavior of a large number of ensembles and understand more clearly their dynamics. The aim of those models is to propose a balanced model between mathematical tractability and biological plausibility while still reproducing a wide range of empirical data features across multiple measurement modalities. These features include: fast oscillations in local field potential (LFP) and extracranial electromagnetic (MEG, EEG) signals; slow quasi-periodic activity fluctuations in haemodynamic (BOLD fMRI, fNIRS) signals; inter-regional synchrony/covariance (‘functional connectivity’) and causal interactions (‘effective connectivity’) in both fast and slow activity patterns; sensory- or electromagnetic stimulation-evoked response waveforms; graph-theoretic properties large-scale network activity; and many others.

Presenter

Sorenza Bastiaens
CAMH Toronto, Ontario
Canada


2) In Action: Cognitive Systems and Applications

Waves or Networks at the basis of Cognition?

Time: 12:45-13:25

How the cognition ‘emerges’ from the physical structure of the brain remains unclear. Neuroimaging studies reveal signatures of cognition in the dynamics of large-scale functional networks, whose origin and generative mechanisms remain under debate. Efforts have been made to link the formation of functional networks at the macroscale to neuronal activity at the microscale using whole-brain computational models. While these models have served to support distinct mechanistic hypothesis for the genesis of functional networks, an alternative hypothesis relating functional networks to resonance phenomena is emerging. In my talk I will discuss these two distinct perspectives, reinforcing the importance to maintain openness to different mechanistic hypothesis while new evidence is needed to disambiguate current conflicts.

Presenter

Joana Cabral
University of Minho Braga
Portugal


Brain geometry and dynamics

Time: 13:25-14:05

The dynamics of many physical systems are naturally constrained by their underlying structure. Here, I will show that the nervous system is no exception, with geometric eigenmodes derived from the brain’s cortical and subcortical geometry accurately capturing diverse experimental human functional magnetic resonance imaging (fMRI) data from spontaneous and task-evoked recordings. Moreover, these geometric constraints are unique to each individual and universally exist across different species. Finally, I will show that the close link between geometry and function is explained by a dominant role of wave-like activity, and that wave dynamics can reproduce numerous canonical features of functional brain organization. These findings identify a previously underappreciated role of geometry in shaping function, as predicted by a unifying and physically principled model of brain-wide dynamics.

Presenter

James Pang
Monash University Melbourne, Victoria
Australia


Modelling of brain stimulation to unveil signal propagation and network dynamics

Time: 14:05-14:45

The human brain comprises distinct resting-state networks (RSNs) characterized by spontaneous activity patterns. Despite this highly structured functional pattern, its laws of motion and principles of organization have proven challenging to understand with currently available measurement techniques. In such epistemic circumstances, an extremely elegant modus operandi to investigate brain complexity with high spatial and temporal resolution entails the administration of precise and synchronized external stimulation, followed by a meticulous examination of the resulting induced propagation dynamics that emerge in response to these perturbations. In this framework, a combination of empirical stimulus-evoked data analyses and whole-brain, connectome-based neurophysiological modelling provide an elegant scaffold to investigate questions around the physiological basis and spatiotemporal network dynamics of RSNs’ activity. Deciphering this evoked propagation pattern is essential for a comprehensive understanding of the brain's response to stimulation and therefore for personalized and targeted interventions, with potential applications ranging from therapeutic treatments to cognitive enhancement.

Presenter

Davide Momi
Precision Neurotherapeutics Lab, Stanford University
USA


3) Beyond Theory: Clinical Applications and Practice

Psychedelic Drugs and Brain Dynamics: Unveiling Turbulent Signatures and Control Energy Landscape Flattening

Time: 16:00-16:30

Psychedelics like LSD and psilocybin alter subjective experience through serotonin 2A (5-HT2A) receptor agonism, resulting in increased brain entropy. We propose this heightened entropy reflects a flattening of the brain's control energy landscape. Using fMRI data, we show that LSD and psilocybin reduce control energy for brain state transitions, leading to more state changes and increased entropy. Analysis tying 5-HT2A receptor distribution to control energy supports this link. Our study reveals how psychedelics facilitate state transitions and diverse brain activity and demonstrates the potential of receptor-informed network control theory. Additionally, psychedelics show promise as treatments for neuropsychiatric disorders. We explored how LSD and psilocybin impact the brain's functional hierarchy using a novel turbulence framework. Both psychedelics produced distinct turbulence-based changes, affecting higher-level networks, especially the default mode network. These findings support the hypothesis that psychedelics modulate the brain's functional hierarchy and offer quantification for two different psychedelics, with potential implications for therapy.

Presenter

Josefina Cruzat
Universidad Adolfo Ibañez Santiago de Chile
Chile


Neurocomputational modelling for predicting outcomes and characterizing neurocognitive pathophysiology in youth at clinical high risk for psychosis

Time: 16:30-17:10

Schizophrenia is a debilitating psychiatric disorder that imposes significant socio-economic burdens on individuals and society. Thus, early identification of those at "clinical high risk" (CHR) of developing schizophrenia is imperative. While interventions at this stage can potentially thwart the onset of schizophrenia or related psychotic disorders, even those CHR patients who do not go on to develop schizophrenia frequently continue to experience high symptom burden and functional impairment. Thus, there is a compelling need to elucidate additional prognostic indicators for this cohort to prioritize treatments. Rationale and Study Aims: A promising avenue is employing neurophysiological measures, specifically, event-related brain potentials (ERPs). Two particular ERPs, the mismatch negativity (MMN) and the N400 semantic priming effect, have been shown to predict conversion to psychosis (1) and decline in psychosocial functioning 1-year later (2), respectively in CHR individuals. Despite the potential of these ERP biomarkers, their neural mechanisms remain largely unknown, thus limiting their clinical utility. Methods: In this study, we address this by fitting a connectome-based neural mass model (CNMM) (3) to both auditory MMN and N400 datasets in a group of N=47 CHR individuals, whose symptoms and general social and role functioning was assessed at baseline and 1-year later (4,5). In this CNMM model, neural dynamics at each source are described by Jansen-Rit (JR) equations (6,7), which encapsulate neural dynamics across three populations: pyramidal neurons, excitatory, and inhibitory interneurons, forming a circuit with one positive and one negative feedback loop. After fitting the CNMM model to participants’ grand averaged ERPs across the two datasets, we extracted the local gain parameters (C1-C4) and used them to predict changes in symptoms and psychosocial functioning.Results: In the MMN task, we found that increased excitation via increased excitatory-to-pyramidal connectivity, was observed in the primary auditory, middle cingulate, and inferior frontal areas, and was associated with an increase in positive symptoms one year later (r= 0.53, p = 0.019). In the N400 priming dataset, heightened disinhibition characterized by a decrease in inhibitory-to-pyramidal and an increase in pyramidal-to-excitatory connectivity—across the occipital, precuneus, middle cingulate, and inferior frontal regions was linked to diminished social and role functioning after one year (r= 0.42, p = 0.031; r= 0.48, p = 0.012), respectively. These results provide support for a neurophysiological model of the psychosis prodrome, linking excitatory-inhibitory mechanisms, brain connectivity, clinical symptoms, and long-term functional outcomes.

Presenter

Andreea Diaconescu
University of Toronto Toronto, Ontario
Canada



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